The present invention relates generally to a cognitive proximate recommendation method, and more particularly, but not by way of limitation, to a system, method, and recording medium for recommending an item in response to a user query having a lowest proximal distance between values of extracted features of the items and the requested item by the user.
Industry is trending towards so called “cognitive models” enabled via “Big Data” platforms. Such cognitive models are aimed to remember prior interactions with users and continuously learn and refine the responses for future interactions. For example, cognitive agents are being used for welcoming customers at business door steps and are expected to evolve intelligent with generations. Such agents could be enriched for better customer handling by building the intelligence of the agents.
Conventional cognitive models for searching and returning answers have proposed searching for information within social networks. The conventional search assist techniques receive a query, such as a partial query, identifies two or more categories of data that include information satisfying the query, ranks the identified categories of data based on various selection criteria, and presents suggested search terms based on the rankings. However, the conventional techniques relate to a display of the results, not the selection in that the conventional techniques rank the results of the query on two or more identified categories and calculate a quality matrix that is used to display results. The conventional techniques do not intelligently learn to provide best alternatives when a null response may occur.
That is, there is a technical problem in that the conventional techniques do not consider a cognitive way of determining a best alternative when a match does not exist and do not consider using user preferences to weigh values of features of potential results to intelligently provide a better alternative.